Smart glasses based detection of atm fraud

ABSTRACT

Systems, methods, and apparatus are provided for fraud screening via smart glasses interactions during an ATM session. A smart glasses device may capture an image of an ATM environment. The ATM and the smart glasses device may be edge nodes on an edge network. An edge platform may use a fraud detection model to classify the image and compare it to stored ATM images. The model may be trained at an enterprise server and stored on the edge platform. In response to a determination of fraud at the edge platform, a fraud alert may be transmitted to the smart glasses device during the ATM session. Edge computing reduces latency to enable real-time smart glasses alerts. The smart glasses device may communicate the fraud alert to other smart glasses devices on the edge network.

FIELD OF TECHNOLOGY

Aspects of the disclosure relate to detecting ATM fraud using smartglasses technology.

BACKGROUND OF THE DISCLOSURE

Annual losses from electronic crime associated with automated tellermachine (ATM) use may reach billions of dollars. One prevalent form ofATM tampering is the practice of “skimming.” Skimming typically involvesmodifying an ATM to capture information associated with a user accesscard. For example, a card reader device may be inserted over or withinthe original ATM card reader, a hidden camera may capture PIN entries,or a keypad overlay may capture keypad strokes. Because thesemodifications appear to be legitimate components of the ATM, skimmingoften goes undetected by users.

Smart glasses may be defined as wearable glasses that include bothhardware and software components. Smart glasses may adopt the structureof a conventional pair of eyeglasses with a frame and lenses. Amicroprocessor may be embedded within the glasses and may provideprocessing capabilities.

It would be desirable to leverage smart glasses capabilities fordetection of fraud at an ATM. It would be desirable to use edgecomputing architecture to reduce latency and provide real-time fraudupdates at a smart glasses interface.

SUMMARY OF THE DISCLOSURE

Systems, methods, and apparatus for smart glasses based detection of ATMfraud are provided.

Data from an ATM and may be stored in an ATM profile. The ATM profilemay include transaction data, fraud data and ATM image data. The ATMprofile data may be stored at an edge computing node. Updated profiledata may be transmitted to enterprise systems at periodic intervals.

A smart glasses user may initiate an ATM session via a smart glassesdevice. The smart glasses device may be authenticated to the edgenetwork. Following authentication, the smart glasses device may capturean image of the ATM environment and transmit the image to the edgecomputing platform.

Machine learning algorithms may classify the smart glasses image basedon the image attributes and compare the image attributes to data fromthe ATM profile. A fraud detection model generated at an enterprisesystem and stored at the edge computing platform may incorporate the ATMprofile data.

When the probability of correspondence between the image attributes fromthe smart glasses image and the ATM profile data meets or exceeds apredetermined threshold, the edge computing platform may transmit anall-clear message to the smart glasses device.

When the probability of correspondence between the image attributes fromthe smart glasses image and the ATM profile does not meet thepredetermined threshold, the edge platform may transmit a fraud alert tothe smart glasses device. The edge computing platform may transmitinstructions to the ATM to disable one or more ATM functions.

The edge computing platform or the smart glasses device may communicatethe fraud alert to other smart glasses devices on the edge network.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and advantages of the disclosure will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows illustrative apparatus and a scenario in accordance withprinciples of the disclosure;

FIG. 2 shows illustrative apparatus and architecture in accordance withprinciples of the disclosure;

FIG. 3 shows an illustrative process flow in accordance with principlesof the disclosure;

FIG. 4 shows illustrative architecture in accordance with principles ofthe disclosure;

FIG. 5 shows illustrative apparatus in accordance with principles of thedisclosure; and

FIG. 6 shows illustrative apparatus in accordance with the principles ofthe disclosure.

DETAILED DESCRIPTION

Systems, methods and apparatus for smart glasses based ATM frauddetection are provided.

Self-service kiosks such as an automated teller machine (ATM) improveaccessibility and efficiency for financial transactions. However, theautonomous nature of such kiosks may leave them vulnerable to tampering.A bad actor may modify the physical environment to skim accessinformation in a way that is undetectable by the user.

It would be desirable to provide smart glasses based capabilities foridentifying tampering or other types of fraud at a self-service kiosksuch as an ATM. It would be desirable to reduce latency via edgeprocessing so that mitigation measures may be taken within a live ATMsession before the user begins a transaction.

For the sake of illustration, the invention will be described as beingperformed by a “system.” The system may include one or more features ofapparatus and methods that are described herein and/or any othersuitable device or approach.

The system may include wearable smart glasses. The smart glasses may bestructured with a frame and lenses. The frame and/or lenses may includeembedded or partially embedded hardware and software components.

The smart glasses may include one or more microprocessors. The smartglasses may include one or more software applications. The applicationsmay enable the smart glasses to execute various tasks. One or more ofthe software applications may be executed on the processors. Softwareapplications may be stored in a memory embedded in the smart glasses.

The smart glasses may include one or more displays. In some embodiments,a smart glasses display may add data alongside the view through thelenses using augmented reality technology. A display controller may beconfigured to display data as a semi-transparent overlay appearing onthe lenses. Augmented reality displays may be achieved through curvedmirror techniques. Alternative techniques include waveguide-basedtechnology such as a virtual retinal display.

The smart glasses may include one or more communication transceivers.The communication transceivers may be operable to communicate with anexternal processor. The external processor may be located within amobile device or any other suitable computing device.

The smart glasses may include a nano wireless network interface card(“NIC”). The nano wireless NIC may provide the smart glasses with adedicated, full-time connection to a wireless network. The nano wirelessNIC may implement the physical layer circuitry necessary forcommunicating with a data link layer standard, such as Wi-Fi. The nanowireless NIC may support input/output (“I/O”), interrupt, direct memoryaccess, interfaces, data transmission, network traffic engineeringand/or partitioning.

The smart glasses may include a wireless controller application. Thewireless controller application may be configured to interface betweenthe NIC and an external Wi-Fi device. The wireless controllerapplication may be configured to transmit data collected by the smartglasses over the wireless network.

The smart glasses may include an active near field communication (“NFC”)reader configured to establish contactless communication with anotherdevice located within a predetermined proximity to the smart glassesdevice. In some embodiments, one smart glasses device may communicatewith another smart glasses device using NFC technology.

The smart glasses may include an embedded subscriber identificationmodule (“E-SIM”) card. The E-SIM may enable the smart glasses tocommunicate and share data with another pair of smart glasses. The smartglasses may include one or more wired and/or wireless communicationapplications such as Bluetooth™. Smart glasses may utilize cellulartechnology or Wi-Fi to be operable as wearable computers runningself-contained mobile applications.

The smart glasses may include a battery. The battery may be configuredto power hardware components such as the microprocessor and the display.The battery may be rechargeable. The battery may be recharged via anysuitable method. Illustrative charging methods include solar charging,wireless inductive charging, and connection via a charging port.

The smart glasses may include one or more cameras for capturing imagesand/or videos, one or more audio input devices, and one or more audiooutput devices.

Smart glasses inputs from a user may be hands-on. Smart glasses inputsfrom a user may be hands-free. In some embodiments, smart glasses inputsmay involve a combination of hands-on and hands-free protocols.

In some embodiments, the smart glasses inputs may be hands-on. The smartglasses may require the use of touch buttons on the frame. In someembodiments, the user input may also be entered via a nano touch screenincorporated into the frame or lenses of the smart glasses. The nanotouch screen may be a nano light emitting diode (“LED”) touch screen.The nano touch screen may be a nano organic light emitting diode(“OLED”) touch screen.

The touch screen may receive touch-based user input. As such, the nanoLED touch screen may cover a portion of the frames and/or lenses of thesmart glasses. Touch-based gestures may include swiping, tapping,squeezing and any other suitable touch-based gestures or combination oftouch-based gestures.

In some embodiments, the smart glasses inputs may be hands-free. Thesmart glasses may receive hands-free input through voice commands,gesture recognition, eye tracking or any other suitable method. Gesturerecognition may include air-based hand and/or body gestures. Air-basedgestures may be performed without touching the smart glasses.

The smart glasses may include one or more sensors. Illustrative sensorsmay include a touch screen, camera, accelerometer, gyroscope and anyother suitable sensors. The smart glasses sensors may detect hands-freeinput such as air gestures or eye movement.

The smart glasses may function as a node on an Internet of Things (IOT)network. An IOT may be defined as a pervasive and ubiquitous networkthat enables monitoring and control of the physical environment bycollecting, processing, and analyzing the data generated by nodes (e.g.,smart objects). The diverse nature and large volume of data collected bynumerous interconnected nodes on an IoT potentially enables uniquefunctionality and operational opportunities.

Each IOT node may represent an IOT device. Each node may include two ormore nodes. A node may include a sensor. Sensors may include devicesthat detect changes in a physical or virtual environment. Sensors mayinclude cameras for capturing images of the environment. For example,one or more cameras may be embedded or partially embedded in smartglasses.

Sensors may be any suitable size. For example, sensors may be a fewmillimeters in size. Sensors may be deployed in a wide variety oflocations. Sensors may be relatively inexpensive and have low energyconsumption. Sensors may “sense” two or more stimuli or environmentalchanges.

Captured data may be transmitted using any suitable transmission method.Captured data may be transmitted by the sensor and processed away fromthe location of the sensor that captured the data. For example, captureddata may be transmitted from one node to another node until the captureddata reaches a data repository.

Captured data may be transmitted to a location where information isneeded for decisioning or consumption, which may not be the same placethe data was captured or generated. Data synchronization protocols andcaching techniques may be deployed to ensure availability of informationat, or delivery to, a desired node. Captured data may be stored locallyon the sensor for an amount of time prior to transmission or broadcastto another node.

In some embodiments, sensor data may be transmitted continuously. Insome embodiments, sensor data may be transmitted on a periodic schedule.In some embodiments, sensor data may be transmitted in response to achange in conditions detected by the sensor.

The sensor data may be processed using edge computing. Edge computing isa distributed, open IT architecture that features decentralizedprocessing power. Data may be processed by a local computer or serverrather than being transmitted to a data center, reducing internetbandwidth usage.

The ATM and the smart glasses may function as edge nodes. Data obtainedby the smart glasses and the ATM may be processed locally using edgecomputing. Edge computing enables real time processing of sensor datawith reduced latency and allows the devices to immediately identify afraud situation on-site. The IoT network may be a local edge network.

The system may include a local edge computing platform. The edgecomputing platform may communicate with the ATM, the smart glasses, andany other suitable device. The edge computing platform may send updatesto other edge nodes and receive updates from other edge nodes. The edgecomputing platform may connect with an enterprise server. The edgecomputing platform may function as an edge gateway for connecting to theinternet.

The edge computing platform may maintain profiles for each ATM. ATMprofiles may include location, ATM images, and ATM fraud data. The ATMprofiles may be updated on a periodic basis.

In some embodiments, ATM profile data from multiple ATMs or multipleedge platforms may be consolidated at an ATM cluster node. A cluster mayinclude a set of ATMs in a defined geographic area. The cluster nodesmay be nodes on the edge network.

The system may include an enterprise server. The enterprise server mayinclude a fraud recognition engine. The fraud recognition engine may useone or more machine learning algorithms to generate a fraud detectionmodel. Data used to train the model may include customer images capturedby smart glasses, ATM images, ATM network fraud data, and fraud datareported by users. Data used to train the model may include ATM profiledata. The fraud detection model may be retrained on a periodic basis orin response to data updates.

The fraud detection model may be a machine learning model. Machinelearning models may be mathematical algorithms trained to makeassumptions about input data. Using the assumptions, the machinelearning model may approximate properties of the input information tocalculate new properties or determine how to respond.

Deep learning is a subset of machine-learning. Deep learning classifiersare input during a training stage as labeled training data. Deeplearning uses the classifiers to learn from the input data and uses thelearned information to correctly classify unlabeled data duringexecution. A deep learning classifier creates, absent human interaction,a non-linear, multi-dimensional classification model based on thelabeled-training data.

Deep learning classifiers typically utilize a layered structure ofalgorithms known as an artificial neural network (“ANN”) to create thenon-linear, multi-dimensional classification model. An ANN mimics thebiological neural network of the human brain. The ANN is comprised oflayers of neurons. Each neuron, included in each layer, takes one ormore inputs and produces one output. The output is based on the valuesof the inputs as well as a weight associated with each inputted value.As such, one input can carry more weight than another input.

The machine learning framework may include a convolution neural network(CNN) that combines a joint feature extractor, classifier and regressortogether in a unified framework.

A CNN is particularly suited for processing images because it convolveslearned features with input data and uses two-dimensional convolutionallayers for classification. A CNN typically begins with an input imageand applies different filters to generate a feature map. Other layersapply functions to increase non-linearity, apply pooling, and input apooled image vector into a fully connected artificial neural network.Images may iterate through the layers until a well-defined neuralnetwork with weights and feature detectors is established.

The enterprise server may periodically receive updated ATM data from theedge computing platform or from a cluster node. The update may be anupdated ATM profile. The update may be transmitted on a daily, weekly,or monthly basis, or on any suitable schedule. The update may betransmitted in response to receipt of new ATM data at the edge computingplatform or cluster node. The update may be transmitted in response to arequest from the enterprise server. Updated ATM data may be used by thefraud recognition engine to retrain the fraud detection model.

The edge computing platform may receive a copy of the fraud detectionmodel from the fraud recognition engine. The fraud recognition enginemay periodically update the fraud detection model maintained at the edgecomputing platform. The update may be transmitted on a daily, weekly ormonthly basis, or on any suitable schedule.

At an ATM, a user may connect to the enterprise network using smartglasses. In some embodiments, the smart glasses may be voice-enabled andthe ATM may authenticate the user via voiceprint analysis or voice-basedinput of an authentication code. The ATM may authenticate the user via atouch-based input to the smart glasses, an air-based gesture, by anycombination of voice, touch and air gestures, by iris recognition, or byany suitable method.

Following authentication of the smart glasses device, an ATM session maybe initiated. In some embodiments, a user may communicate with the ATMvia voice-based input to the smart glasses device.

Images of the ATM and the surrounding environment may be captured by thesmart glasses camera and transmitted over a local network to the edgecomputing platform.

The edge computing platform may process the images and categorize thedata. The edge computing platform may store image color, angle,direction, aspect ratio and any other relevant data in a dataset. One ormore deep learning algorithms may be applied for image classificationand/or object identification. In some embodiments, a CNN may be applied.

The edge computing platform may identify ATM fraud based on the imagecaptured by the smart glasses. The image may be compared to ATM profiledata. Machine learning algorithms may take image objects as input andprocess the images in real time. The edge computing platform mayidentify ATM fraud during a live interaction with a smart glasseswearer.

The system may identify ATM features in the smart glasses image. Thesystem may compare the ATM features identified in the image to storedparameters for these features. Based on the comparison, the system mayidentify fraud. For example, an ATM card reader may be modified by a badactor to skim data from cards entered at the machine. The modificationsmay not be apparent to the user, but based on the image captured by thesmart glasses, the system may detect an anomaly. In another example, abad actor may install a camera in the ATM environment to capture userPIN entry. The camera may be camouflaged to appear as part of the ATMand may not be apparent to the user. Based on the image captured by thesmart glasses, the system may detect the camera and trigger a fraudalert.

The determination of fraud may be based on a percentage correlationbetween the image captured by the smart glasses and the ATM profiledata. If the percentage correlation meets or exceeds a predeterminedthreshold, the system may determine that there is no detectable fraud.If the percentage correlation does not meet a predetermined threshold,this may indicate modifications to the ATM and trigger a determinationof fraud.

The determination of fraud may be based on image data from the ATMprofile. The determination of fraud may be based on fraud data ortransaction data from the ATM profile. The determination of fraud may bebased on user reported fraud data. The determination of fraud maycombine image analysis with other ATM profile data and/or user reporteddata.

Edge processing enables real time communication with the smart glasseswearer at the ATM with reduced latency. The smart glasses may captureand transmit an image of the ATM environment and the edge platform mayreturn a result before the user begins a transaction.

In some cases, the edge computing platform may determine that there isno fraud risk at the ATM. The edge computing device may communicate tothe smart glasses that it is safe for the user to proceed with atransaction. An all-clear message may be communicated to the user as anaudio message generated by the smart glasses. The all-clear message maybe displayed to the user on the lenses of the smart glasses using anaugmented reality display.

In other cases, the edge computing platform may determine that there isa fraud risk at the ATM. The edge computing device may communicate afraud alert to the smart glasses. The fraud alert may be communicated tothe user as an audio message generated by the smart glasses. The fraudalert may be displayed to the user on the lenses of the smart glassesusing an augmented reality display.

In some embodiments, along with a fraud alert, the edge computing devicemay transmit directions to an alternate ATM location to the smartglasses. The directions may be communicated to the smart glasses uservia audio, augmented reality display or via any suitable method. In someembodiments, the directions may be displayed on the ATM screen.

In response to a determination of fraud, the system may disable one ormore ATM functions. In some embodiments, the edge computing platform maytransmit instructions to the ATM to disable transaction capabilities.Other mitigation options may include powering down the ATM, displaying awarning on the ATM screen or locking an access card slot.

The edge computing platform may communicate a fraud alert to other smartglasses devices functioning as nodes on the IOT network. The smartglasses may communicate the information on a smart glasses display, viaan audio alert, or by any suitable method. In some embodiments, onesmart glasses node may share fraud information directly with other smartglasses nodes. Transmission over the local edge network may enablewidespread fraud notification for smart glasses users that are not atthe time engaged in a transaction.

The edge computing platform may transmit categorized image data to thefraud recognition engine for comparison with previously categorized datamaintained on the enterprise server. The edge computing platform maytransmit a determination of fraud to the enterprise server. Data fromthe edge computing platform may be used by the fraud recognition engineto update and retrain the fraud detection model.

One or more non-transitory computer-readable media storingcomputer-executable instructions are provided. When executed by aprocessor on a computer system, the instructions perform a method forreal-time fraud screening via smart glasses interactions during an ATMsession.

The method may include receiving a fraud detection model generated at anenterprise server, the model based on data from the ATM. The method mayinclude receiving a request to initiate an ATM session from a smartglasses device and authenticating the smart glasses device.

The method may include receiving an image of the ATM environmentcaptured by the smart glasses device during the ATM session andclassifying the smart glasses image based on more image attributes. Thefraud detection model may be applied to compare the image attributesfrom the smart glasses device to the data received from the ATM.

When the probability of correspondence between the image attributes andthe data from the ATM meets or exceeds a predetermined threshold, themethod may include transmitting an all-clear message to the smartglasses device.

When the probability of correspondence between the image attributes andthe data from the ATM does not meet the predetermined threshold, themethod may include transmitting a fraud alert to the smart glassesdevice and disabling one or more ATM functions.

Systems, methods, and apparatus in accordance with this disclosure willnow be described in connection with the figures, which form a parthereof. The figures show illustrative features of apparatus and methodsteps in accordance with the principles of this disclosure. It is to beunderstood that other embodiments may be utilized, and that structural,functional and procedural modifications may be made without departingfrom the scope and spirit of the present disclosure.

The steps of methods may be performed in an order other than the ordershown and/or described herein. Method embodiments may omit steps shownand/or described in connection with illustrative methods. Methodembodiments may include steps that are neither shown nor described inconnection with illustrative methods. Illustrative method steps may becombined. For example, an illustrative method may include steps shown inconnection with any other illustrative method.

Apparatus may omit features shown and/or described in connection withillustrative apparatus. Apparatus embodiments may include features thatare neither shown nor described in connection with illustrativeapparatus. Features of illustrative apparatus may be combined. Forexample, an illustrative apparatus embodiment may include features shownor described in connection with another illustrative apparatus/methodembodiment.

FIG. 1 shows illustrative scenario 100. In scenario 100, smart glassesdevice 104 is used at ATM 102. Smart glasses device 104 may beauthenticated for access to the ATM. Smart glasses device 104 maycommunicate directly with the ATM. In some embodiments, the user mayenter a PIN or authentication code to authenticate smart glasses device104. The PIN may be entered via smart glasses device 104 as avoice-based input, touch-based input, gesture-based input or as anycombination of inputs. Following authentication of the smart glassesdevice, the device may capture an image of the ATM environment andtransmit the image to an edge computing platform (not shown).

FIG. 2 shows illustrative architecture 200. Architecture 200 may includeapparatus shown in scenario 100. Illustrative architecture 200 includeslocal edge network 206, which connects edge nodes 204 and 210 to edgeplatform 208. Edge platform 208 may connect to the internet (not shown).

Smart glasses device 204 may capture an image of ATM 202. Smart glassesdevice 204 may transmit the image via local network 206 to edge platform208 for processing. In some embodiments, the edge network may be a widearea network (WAN) or any suitable network configuration. Edge platform208 may determine that there is a likelihood of fraud at ATM 202. Edgeplatform 208 may transmit a fraud alert to smart glasses device 204.Edge platform 208 may transmit the fraud alert to smart glasses devices210 which are not in use at the ATM. In some embodiments, smart glassesdevice 204 may communicate the fraud alert directly to smart glassesdevices 210. The fraud alert may include directions to an alternate ATMlocation.

FIG. 3 shows illustrative process flow 300 for smart glasses basedidentification of ATM fraud. At step 302, the smart glasses device isauthenticated to the ATM. At step 304, an ATM session is initiated. Atstep 306, the smart glasses device captures an image of the ATMenvironment.

At step 308, an edge computing platform analyzes the image. The imagemay be compared to stored ATM images. The platform may use one or moremachine learning algorithms to classify and compare the image. At step310, a fraud detection model may be generated and trained at theenterprise server and stored at the edge platform. The edge platform maytransmit ATM data, smart glasses images, and fraud determinations forongoing training of the model.

At step 312, the edge computing platform may determine, based on thecaptured image, whether there is a risk of fraud at the ATM. If fraud isnot detected, at step 314, the edge computing platform may transmit anall-clear message to the smart glasses device. If fraud is detected, atstep 316, the edge computing device may transmit a fraud alert to thesmart glasses device.

FIG. 4 shows illustrative architecture 400. Architecture 400 includesedge network 406 (including elements 408-416) and enterprise system 402.Edge network 406 may use internet 418 to communicate with enterprisesystems.

Enterprise system 402 may include fraud recognition engine 404. Fraudrecognition engine 404 may train a machine learning model for frauddetection at an ATM. The fraud recognition engine may use data receivedfrom the edge network to train the machine learning model.

Edge network 406 may include edge computing platform 408. Edge network406 may include edge nodes 416 and 414. The edge computing platform maystore ATM data received from edge nodes 414. The edge computing platformmay store a copy of the fraud detection model received from enterprisesystem 412.

FIG. 5 is a block diagram that illustrates a computing device 501(alternatively referred to herein as a “server or computer”) that may beused in accordance with the principles of the invention. The computerserver 501 may have a processor 503 for controlling overall operation ofthe server and its associated components, including RAM 505, ROM 507,input/output (“I/O”) module 509, and memory 515.

I/O module 509 may include a microphone, keypad, touchscreen and/orstylus through which a user of device 501 may provide input, and mayalso include one or more of a speaker for providing audio output and avideo display device for providing textual, audiovisual and/or graphicaloutput. Software may be stored within memory 515 and/or other storage(not shown) to provide instructions to processor 503 for enabling server501 to perform various functions. For example, memory 515 may storesoftware used by server 501, such as an operating system 517,application programs 519, and an associated database.

Alternatively, some or all of computer executable instructions of server501 may be embodied in hardware or firmware (not shown).

Server 501 may operate in a networked environment supporting connectionsto one or more remote computers, such as terminals 541 and 551.Terminals 541 and 551 may be personal computers or servers that includemany or all of the elements described above relative to server 501. Thenetwork connections depicted in FIG. 1 include a local area network(LAN) 525 and a wide area network (WAN) 529, but may also include othernetworks.

When used in a LAN networking environment, computer 501 is connected toLAN 525 through a network interface or adapter 513.

When used in a WAN networking environment, server 501 may include amodem 527 or other means for establishing communications over WAN 529,such as Internet 531.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variouswell-known protocols such as TCP/IP, Ethernet, FTP, HTTP and the like ispresumed, and the system may be operated in a client-serverconfiguration to permit a user to retrieve web pages from a web-basedserver. Any of various conventional web browsers may be used to displayand manipulate data on web pages.

Additionally, application program 519, which may be used by server 501,may include computer executable instructions for invoking userfunctionality related to communication, such as email, short messageservice (SMS), authentication services and voice input and speechrecognition applications.

Computing device 501 and/or terminals 541 or 551 may also be mobileterminals including various other components, such as a battery,speaker, and antennas (not shown). Terminal 551 and/or terminal 541 maybe portable devices such as a laptop, tablet, smartphone or any othersuitable device for receiving, storing, transmitting and/or displayingrelevant information.

Any information described above in connection with database 511, and anyother suitable information, may be stored in memory 515. One or more ofapplications 519 may include one or more algorithms that encryptinformation, process received executable instructions, interact withenterprise systems, perform power management routines or other suitabletasks. Algorithms may be used to perform the functions of one or more ofgenerating an ATM profile, updating a fraud detection model, classifyingan image, transmitting a fraud alert, and/or perform any other suitabletasks.

The invention may be operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, tablets, mobile phones and/or other personal digitalassistants (“PDAs”), multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

FIG. 6 shows an illustrative apparatus 200 that may be configured inaccordance with the principles of the invention.

Apparatus 600 may be a computing machine. Apparatus 600 may include oneor more features of the apparatus that is shown in FIG. 5 .

Apparatus 600 may include chip module 602, which may include one or moreintegrated circuits, and which may include logic configured to performany other suitable logical operations.

Apparatus 600 may include one or more of the following components: I/Ocircuitry 604, which may include a transmitter device and a receiverdevice and may interface with fiber optic cable, coaxial cable,telephone lines, wireless devices, PHY layer hardware, a keypad/displaycontrol device or any other suitable encoded media or devices;peripheral devices 606, which may include counter timers, real-timetimers, power-on reset generators or any other suitable peripheraldevices; logical processing device 608, which may generate ATM profiles,train or update a fraud detection model, classify images, generate fraudalerts, and perform other methods described herein; and machine-readablememory 610.

Machine-readable memory 610 may be configured to store inmachine-readable data structures: ATM profile data, one or more machinelearning models, smart glasses images, fraud determinations, and anyother suitable information or data structures.

Components 602, 604, 606, 608 and 610 may be coupled together by asystem bus or other interconnections 612 and may be present on one ormore circuit boards such as 620. In some embodiments, the components maybe integrated into a single chip. The chip may be silicon-based.

Thus, methods and apparatus for SMART GLASSES BASED DETECTION OF ATMFRAUD are provided. Persons skilled in the art will appreciate that thepresent invention can be practiced by other than the describedembodiments, which are presented for purposes of illustration ratherthan of limitation, and that the present invention is limited only bythe claims that follow.

What is claimed is:
 1. A method for fraud screening via smart glassesinteractions during an ATM session, the method comprising: receivingdata from an ATM and storing the data in an ATM profile; receiving arequest to initiate an ATM session from a smart glasses device andauthenticating the smart glasses device to initiate the ATM session;receiving an image of the ATM environment captured by the smart glassesdevice during the ATM session; classifying the smart glasses image basedon one or more image attributes; comparing the one or more imageattributes from the smart glasses image to the ATM profile; when aprobability of correspondence between the image attributes from thesmart glasses image and the ATM profile meets or exceeds a predeterminedthreshold, transmitting an all-clear message to the smart glassesdevice; and when the probability of correspondence between the imageattributes from the smart glasses image and the ATM profile does notmeet the predetermined threshold: transmitting a fraud alert to thesmart glasses device; and disabling a transaction capability at the ATM.2. The method of claim 1, wherein the ATM data comprises an ATM image,an ATM location, and ATM transaction data.
 3. The method of claim 1,wherein: the ATM is a first edge node; the smart glasses device is asecond edge node; the ATM data and the smart glasses image aretransmitted to an edge computing platform using a local edge network;and the fraud alert is transmitted to the smart glasses device from theedge computing platform using the local edge network.
 4. The method ofclaim 3, wherein the smart glasses device is configured to transmit thefraud alert to at least one other edge node via the local edge network,the at least one other edge node comprising another smart glassesdevice.
 5. The method of claim 1, wherein the smart glasses devicecomprises a voice controller, the method further comprisingcommunicating the fraud alert to a smart glasses device user as an audiomessage.
 6. The method of claim 1, wherein the smart glasses devicecomprises a display controller, the method further comprisingcommunicating the fraud alert to a smart glasses device user via a smartglasses augmented reality display.
 7. The method of claim 3, furthercomprising periodically transmitting ATM data from the edge computingplatform to a remote enterprise server.
 8. The method of claim 7,further comprising: receiving a fraud recognition model from the remoteenterprise server at the edge computing platform, the fraud recognitionmodel based at least in part on the ATM data; storing the fraudrecognition model at the edge computing platform; and using the fraudrecognition model for classifying and comparing the smart glasses image.9. The method of claim 8, further comprising periodically receiving anupdate to the fraud recognition model at the edge computing platformfrom the remote enterprise server.
 10. The method of claim 1, furthercomprising, when the probability of correspondence between the imageattributes and the ATM profile does not meet the predeterminedthreshold, transmitting travel directions to an alternate ATM locationto the smart glasses device.
 11. One or more non-transitorycomputer-readable media storing computer-executable instructions which,when executed by a processor on a computer system, perform a method forreal-time fraud screening via smart glasses interactions during an ATMsession, the method comprising: receiving a fraud detection modelgenerated at an enterprise server, the model based at least in part ondata from the ATM; receiving a request to initiate an ATM session from asmart glasses device and authenticating the smart glasses device;receiving an image of the ATM environment captured by the smart glassesdevice during the ATM session; classifying the smart glasses image basedon one or more image attributes; using the fraud detection model,comparing the image attributes from the smart glasses image to the datafrom the ATM; when a probability of correspondence between the imageattributes and the data from the ATM meets or exceeds a predeterminedthreshold, transmitting an all-clear message to the smart glassesdevice; and when the probability of correspondence between the imageattributes and the data from the ATM does not meet the predeterminedthreshold: transmitting a fraud alert to the smart glasses device; anddisabling one or more ATM functions.
 12. The media of claim 11, wherein:the ATM is a first edge node; the smart glasses device is a second edgenode; the ATM data and the smart glasses image are transmitted to anedge gateway using a local edge network; and the fraud alert istransmitted to the smart glasses device from the edge gateway using thelocal edge network.
 13. The media of claim 12, further comprisingperiodically transmitting ATM data from the edge gateway to theenterprise server.
 14. The media of claim 11, further comprisingperiodically receiving an update to the fraud detection model from theenterprise server.
 15. The media of claim 11, wherein the data from theATM comprises an ATM image, an ATM location, and ATM transaction data.16. The media of claim 11, wherein the smart glasses device comprises avoice controller, the method further comprising: receiving a voice-basedrequest from a smart glasses device user to initiate the ATM session;and communicating the fraud alert to the smart glasses device user as anaudio message.
 17. The media of claim 12, wherein the smart glassesdevice is configured to communicate the fraud alert to at least oneother edge node using the local edge network, the at least one otheredge node comprising another smart glasses device.
 18. A system forfraud screening via smart glasses interactions during an ATM session,the system comprising: a first edge node comprising an ATM; a secondedge node comprising a smart glasses device; a third edge nodecomprising an edge platform, the third edge node configured to: receiveATM data from the first edge node; generate an ATM profile based atleast in part on the ATM data and transmit the ATM profile to anenterprise server; receive a machine learning model generated by theenterprise server, the model based at least in part on the ATM profile;receive an image of the ATM environment captured by the smart glassesdevice during an ATM session and classify the image based on one or moreimage parameters; input image classification data to the machinelearning model; in response to a determination that a probability ofcorrespondence between the image classification data and the ATM profilemeets or exceeds a predetermined threshold, transmit an all-clearmessage to the smart glasses device; and in response to a determinationthat the probability of correspondence between the image classificationdata and the ATM profile does not meet the predetermined threshold:transmit a fraud alert to the smart glasses device; and transmitinstructions to disable one or more ATM functions.
 19. The system ofclaim 18, wherein, in response to a determination that the probabilityof correspondence between the image classification data and the ATMprofile does not meet the predetermined threshold, the edge platform isconfigured to transmit travel directions to an alternate ATM location tothe smart glasses device.
 20. The system of claim 18, wherein the ATMprofile comprises an ATM image, an ATM location, and ATM transactiondata.